**Machine learning** is relatively new. It is defined as the process of involving computers to learn and do like human beings. The process facilitates human learning by providing them with information and data through observation as well as through real-world interactions. It is defined as part of **artificial intelligence. **This involves the use of algorithm to process data as well as learn from such data.

Through that, it is possible to make a prediction or scientific prediction about the future. The machine can actually act and it does not need to be programmed to act that way. This means that this kind of machine behavior is not founded on the rule of **basic programming**. It is not easy to pinpoint at one accurate definition of **machine learning** and that is because this field is entirely new.

Because it is a new field **machine learning engineers** are rare bred professionals. Machine engineers use data exploration with software engineering. There may not be a single defined path or process through which one can** become a machine learning engineer, **but there are lots of steps which you can take to become one.

You would learn those processes here. You will also learn that after acquiring the training, you can **get a high paying**. Of course, you must qualify to apply for such a job. All the processes are shown here. Keep reading to discover more.

## What is a machine learning engineer?

**Machine learning engineers **are specialists in creating data products and who ensures that data science actually works in product. They are specialists who feed data to the model. They can scale the data to different product levels. One thing to note here is that this is a new role.

This field has not been in existence like other engineering fields. This is a new skill and as said before, professionals in this field are in high stand across the country and even in the world.

Before you can **become a machine, learning engineer** you must first learn it and get the necessary qualifications. You must learn the skills and there are different ways of **learning that skills** that can **qualify to become a machine learning engineer**.

### Requirements for Being a Machine Learning Engineer

To become one, you must first learn the skills and there are different requirements which you must possess before you can even learn those skills. There are some key requirements that intending professionals must meet before they study that course.

One of the basic requirements has to with experience and it means that you must have experience about **applying machine learning** methods to product, services, and features such as pipeline work, tradeoffs as well as minimizing footprints.

**Furthermore, you must be an expert when it comes to python and cc in development.**

There are different areas of machine learning, you must have a background knowledge in at least one of the areas.

Furthermore, you must be skillful in **data and algorithm structure**

You must be skillful when it comes to interpersonal as well as collaboration

Besides, you must be able to learn as well as modify existing codes

All the above points are showing that you must have the **technical skills** before you can pursue this career. This is because the work will cut across those areas mentioned above. This extensively involves the use of different algorithms and you must be working with all these and you would learn how to change them as well.

Most importantly, you must be able to experiment all those algorithms in various **codebases** **and different settings**. This is why it required that you have previous knowledge about this area.

Furthermore, it is necessary that you have a thorough understanding of the newest **deep learning **as well as **machine learning papers**. Furthermore, you should be able to implement the architecture. If you are able to learn some of the skills from cutting edge company it is going to be an advantage because you acquire that skill faster than you think.

Because of that, it is always recommended that you start with a company involved in software creation. Furthermore, try to gain knowledge about machine learning and statistic. You require these **basic skills** if you want to get training as a **machine learning engineer.**

Furthermore, if you are in the field of academic and you have extensive knowledge about the theories and that can assist in developing software engineering knowledge.

If you think that you can do it, then you can teach yourself the software fundamentals as well as **machine learning theories.** Some people can learn this on their own but it is very difficult. It is important that you are guided so that you can achieve your aims with ease.

Furthermore, there is a need to have coding, statistic, and math. These are helpful if you want to **become an expert in machine learning** or you. programming is an indispensable part of that learning and you must be able to do linear algebra and statistics if you hope to do well in this area.

The most important thing is that before you begin there is the need to have a solid foundation on data analysis because a lot is going to depend on that. You must learn some important subjects and some of the subjects to devote your time to learn include statistics. When you learn that, it would be easier for you to understand some data sets when you see one.

There are some institutions that will help you learn those fundamental courses which would qualify you for the main course.

Some of the places you can **learn those qualification courses are Udacity** where you can learn about introductory descriptive statistics. This is where you are going to learn about information data sets.

Another thing that you can learn from Udacity which can qualify you for that course is the introductory courses on inferential statistics. When you learn that, it is going to qualify you for those courses in **machine learning engineering**.

Besides you can register for the getting and cleaning data from **Johns Hopkins University**. You are going to learn a lot of things here and it includes how to obtain as well as optimize data sets.

Moreover, you can learn about feature engineering from Machine Learning and you can** learn this from Udemy**. This is a course that can qualify you for the training. You can learn a lot of things here and one of them is the process and how to manipulate data variables.

**Summary of skills that you need to learn**

The most basic include **Computer science fundamentals and Programming fundamentals**

This is the most important aspect of machine learning professionals. It incorporates a lot of things such as data structures. The aspects it incorporates include queues, stacks, trees, multidimensional arrays, as well as graphs and so on. It also includes different aspects of algorithms which **include sorting, searching, programming, as well as optimization.**

** **This also has to do with complexity and computability such as **P vs. NP, and complete problems, big-O notation, as well as approximate algorithms and so on. **Furthermore, there is the aspect of computer architecture which includes **cache, memory, deadlocks, bandwidth and distributed processes, **and so on.

Another important requirement is probability and statistics. This aspect is also indispensable in **machine learning engineering.** Before you venture into the training program, you must have prior knowledge of this area. This has to do with the formal characterization of probability. It covers different areas and some of them are **Bayes rule, conditional probability, independence, likelihood, **and so on.

This also has to do with techniques that are derived from that. It covers such areas like **Bayes Nets, Hidden Markov, Markov Decision Processes.** These are important aspects of the **machine learning algorithm**. There are uncertainties in the world and there are means of dealing with them. These can serve as a means of dealing with these in the real world. It is important that you have knowledge of these before you venture into **studying machine learning engineering.**

This has a lot in common with statistics. There are certain fields in statistics that you must know about if you want to do well here and they include such areas like measures which include **variance, median **as well as mean and so on. You also need to know about distribution such as** Poisson, binomial**, normal as well as uniform. Moreover, you should be informed about the analysis method like** hypothesis ANOVA** as well as testing methods and so on.

You require this knowledge since they are going to help you in building as well as validating models using observed data. It is obvious that most of the **machine learning algorithms** you meet in the field are taken from the statistical modeling procedures. This means that it has a lot to do with statistics. There is no way you can **become a machine learning engineer** without having a good knowledge of statistics.

### Data modeling and evaluation

Another aspect to learn before you can qualify for the training is **data modeling and evaluation. **This is the process where underlying structure of any dataset. The aim is to discover a useful pattern. The pattern includes eigenvectors, clusters as well as correlations. This is very useful when it comes to predicting properties of unseen instances such as anomaly** detection, regression, classification.**

The aim of the continuous estimation here is to determine the importance of any model. If you want to take a course in **machine learning engineering** and you want to be satisfied with any of the training, you must know much about these aspects. It is one of the requirements.

### Applying machine learning algorithm and libraries

The aspect of this course has to do with **machine learning algorithms and libraries. **You must have sufficient knowledge of that before you can enroll for that course. You must be able to apply this effectively and that implies that selecting the best model.

Furthermore, you have to learn something about system design and software engineering. It is important to note that most of the outcomes of the learning process are delivered through the software. It is important that you have prior knowledge about this before you apply to learn for that course.

You must learn how these processes work and it would become much easier for you to practice after you have undergone the formal training.

### Complete online courses that have to do with machine learning

Once you are able to code as well as understand those fundamental principles that are guiding data exploration, you are good to** start with machine learning**.

There are several subjects which are involved here and they include designing of the **machine learning systems**, implementation of the neural networks as well as creating algorithms. Some of the courses that are required for this include **Machine learning** and this is available at Stanford. Here you would be exposed to the introductory class and this must focus on breaking down those complex concepts that are involved in the field.

Another one is Learning from Data and you can acquire this from Caltech. This is another introductory class and this has to do with mathematical theory as well as algorithm applications.

The other one includes **Practical Machine Learning** and you get this one from **Johns Hopkins University**. The aim of this is to learn data prediction. Furthermore, there is also the **Deep Learning Specialization** and you learn Coursera and the aim is to create a neural network.

There are different places that you can earn the **necessary degrees and certifications.** When you do that, it places you at an advantage because you can easily get a job as an expert. There is no way you can do that or become an expert in this field if you do not have formal education. After graduation, there is also the need to get accreditation and that is going to make you more useful in this field.

There are different places where you can learn that skill and **get that degree certificate. **Here are some of the notable degrees you can earn here to become a professional.

Online Nano degrees in computer science engineering and machine learning.

A certification in machine learning and this is obtained from **University of Washington**

Furthermore, you can obtain an **Artificial Intelligence Graduate Certificate** and this can come from Stanford.

Moreover, there the certification of the Professional Achievement in** Data Science from Columbia University.** Most importantly, you can** learn the Machine learning and Data Mining certification from Harvard University.**

The normal undergraduate and a graduate degree in **computer science and engineering.**

### Machine Learning Engineer Course Certification

Course Duration: | 200+ Hours of Interactive Learning |

No. of Students Enrolled: | 6361 Students |

Price of the Course: | $1,499 |

Levels of Course: | Machine Learning Engineer Certification |

Average Salary | $122k Per year |

Rating: | 4.5 Star (2550) |

Website: | ENROLL NOW |

## Skills needed for machine learning

There are some important skills you need to **become an expert in machine learning.** Some of them were mentioned above but they were exhaustive. It is important that the most important skills you need are revisited.

#### A programming language such as Python/ C++/R/Java

This kind of language is very important if you actually want to get a job as a machine learning engineer. It can help in speeding codes up. R is very important because it works well in plots and statistics. In the same way, Hadoop is based in Java. It is for that you must have **basic language programming skills** especially those that are mentioned above.

#### Statistics and Probability

This has to do with theories and theories that are based on statistics and probability are indispensable when it comes to the algorithm. There are several such theories and some of them were mentioned above. More to the theories include **Gaussian Mixture Models, Naïve Bayes, **as well as Hidden Markov Models.

This implies that if you want to do well in this field that you must have a good understanding of statistics and probability. This skill is very important. Talking about statistics, it is relevant in the algorithm because it is useful in the evaluation and understanding of metrics such as** p-curves, receiver-operator curves, and confusion matrices,** and so on.

#### Data Modeling and Data evaluation

Machine learning has much to do with estimation. The estimation process is useful because it can be used in evaluating various tasks at hand at any point in time. There is the need to select the most accurate data and you must make room for error margin. Some of the **evaluation methods** that can be used here include the** sum-of-square-error for regression, log-loss-for classification, **and so on. In the same way, the evaluation method is an indispensable skill.

Some of the evaluation strategies that can be used for this process include **sequential vs randomized cross-validation, training-testing split,** and so on.

#### Machine learning algorithm

This involves having a thorough understanding of algorithm theory as well as knowing how those algorithm works are very important. In the same way, it makes it easy to understand as well as describe different models such as** SVMs**. To do that, there is the need to understand certain subjects like **convex optimization gradient descent, quadratic programming, **as well as partial differential equations, and so on.

#### Distributed computing

Most of the job’s professionals do here involve large sets of data. It is not possible to have all of them processed with just one machine. There is the need to have the whole thing distributed using the whole cluster. There are some projects that can make that process easy and possible like the **Amazon EC2 **as well as **Apache Hadoop.** It is cost-effective in any case.

#### Advanced signal processing methods

One of the most important aspects of **machine learning is feature extraction**. Because of that, you require important **skills in signal processing**. You are going to encounter different kinds of problems and all these would require different solutions to deal with them. Some of the most advanced processing algorithm skills that you must master include the following, **shearlets, wavelets, bandlets, contourlets,** as well as curvelets and so on.

There are other skills that you must master if you want to be successful in this field and they include the following:

You should be updating yourself. This means that you must learn more to ensure that you are up to date with any changes in the field. If there are changes in tool development that emanate from conferences of changelog such as algorithm, theory and so on, you must be familiar with them. Do not allow such changes to take you unawares.

The best way to remain updated and relevant here is to read always and a lot. Always consult important pages like **Google Big Table, Google File System, **as well as **Google Map, Reduce,** and so on. You can learn a lot and change when there is a need for that in that industry.

## Top 10 Machine Learning Algorithms in 2020

There are different kinds of **machine learning algorithms** that you need to know. Here are the best ten of them.

### 1. Linear regression

This is one of the most popular algorithms. The aim to find out the relationship that exists between two variables. This is done by plotting an equation known as the linear equation through the data. Before they arrive at conclusions various features are observed and analyzed. This equation works well with python. It suggests that it is powerful. There are always two variables here. One of them is explanatory and the other is dependent.

### 2. Logistic regression

Another important algorithm is logistic regression. This is a binomial classifier and this means that it consists of two values or two states. Here you assign just yes or no to them or true or false. Data here are compressed before they are analyzed. It is the opposite of linear because its predictions are made based on nonlinear features.

### 3. Linear discriminant analysis

This involves a combination of linear feature which sets input data apart. The major aim is to find out those variables that are dependable in nature. This is one technique classification. It measures class value as well as the variance within that class. Input data are examined using the independent variables during the analysis stage.

### 4. K-Nearest Neighbors

For beginners this one is great and it is known as kNN. Predictions are easy to make here and it is often based on old available data. It is part of supervises the machine learning list. Based on data it is easier to measure similarities and differences in each case.

### 5. Regression trees

This is also known as decision trees. It is part of the supervised **machine learning algorithms.** It is called trees because they have leaves branches and roots everywhere. It can combine well with Python. The purpose here is for predictive purposes. This is less stressful to arrive at decisions. They are best used for classification problems.

### 6. Random forest

It is another part of supervised machine learning. There are many decision trees. It is not one as in the case of regression trees. The order of appearance is not important when making input. If the trees are larger in number then the results would be more accurate. If it is less, then there would less accurate results.

### 7. AdaBoost

This is another name for adaptive boosting. It was great and it was rewarded in 2003. The analysis is also based on a tree system. It is much different from random forests and decision trees. The differences are in the way they set priorities.

### 8. Naïve Bayes

When the problem is about text classification then Naïve Bayes becomes very important. It is great when there is a high dimensional data set like spam filtration or news articles classification. It goes with signature names since is treats every variable as an independent variable.

### 9. Learning vector quantization

LVQ is one of the most advanced. It represents a neural network algorithm. The aim is to recreate neurology of the human brain. It depends on codebook vectors as a representation and lists the number as well as their inputs and outputs.

### 10. Support vector machines

The SVMs is the most advanced. When there are extreme cases of classifications then you can think of this one.

## How to get a job in machine learning

**Apply for machine learning internship**

One of the ways of **getting jobs here** is to apply for internship. If you do well and you have a good result to present after your graduation. Moreover, if you have accomplished a personal project in school, it can assist you to get a job as an internship and which paves the way for greater jobs in the future.

### Produce a great resume

If you are able to present a great resume that can bring forward your machine learning skills and experiences, it can assist you to get a job. Focus on the things which are relevant to the job you are seeking. This can help you secure the job.

### Create a personalized cover for each job

When you are applying for a job you must create a personalized cover letter for that position that you often apply for. Includes those things which you can bring into the company.

### Submit job applications

Before you get those jobs, you must first submit job applications to the various places you want to work. You must submit as many as possible. Ensure that you provide relevant skill, educational, and well exposure and experiences that can assist you to get that job done. You can be considered because there are lots of opportunities available here.